EAI Endorsed Transactions on Internet of Things

Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities

Thu, 30 Nov 2023 00:00:00 +0000

The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.
In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.

Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements

Syed Khasim, Hritwik Ghosh, Irfan Sadiq Rahat, Kareemulla Shaik, Manava Yesubabu — Mon, 27 Nov 2023 00:00:00 +0000

Microorganisms are pervasive and have a significant impact in various fields such as healthcare, environmental monitoring, and biotechnology. Accurate classification and identification of microorganisms are crucial for professionals in diverse areas, including clinical microbiology, agriculture, and food production. Traditional methods for analyzing microorganisms, like culture techniques and manual microscopy, can be labor-intensive, expensive, and occasionally inadequate due to morphological similarities between different species. As a result, there is an increasing need for intelligent image recognition systems to automate microorganism classification procedures with minimal human involvement. In this paper, we present an in-depth analysis of ML and DL perspectives used for the precise recognition and classification of microorganism images, utilizing a dataset comprising eight distinct microorganism types: Spherical bacteria, Amoeba, Hydra, Paramecium, Rod bacteria, Spiral bacteria, Euglena and Yeast. We employed several ml algorithms including SVM, Random Forest, and KNN, as well as the deep learning algorithm CNN. Among these methods, the highest accuracy was achieved using the CNN approach. We delve into current techniques, challenges, and advancements, highlighting opportunities for further progress.

Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation

Koneru Pranav Sai, Sagar Dhanraj Pande — Tue, 28 Nov 2023 00:00:00 +0000

INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation.

OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities.

METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach.

CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.

A Comparative Analysis of Various Deep-Learning Models for Noise Suppression

Henil Gajjar, Trushti Selarka, Absar M. Lakdawala, Dhaval B. Shah, P. N. Kapil — Wed, 29 Nov 2023 00:00:00 +0000

Excessive noise in speech communication systems is a major issue affecting various fields, including teleconferencing and hearing aid systems. To tackle this issue, various deep-learning models have been proposed, with autoencoder-based models showing remarkable results. In this paper, we present a comparative analysis of four different deep learning based autoencoder models, namely model ‘alpha’, model ‘beta’, model ‘gamma’, and model ‘delta’ for noise suppression in speech signals. The performance of each model was evaluated using objective metric, mean squared error (MSE). Our experimental results showed that the model ‘alpha’ outperformed the other models, achieving a minimum error of 0.0086 and maximum error of 0.0158. The model ‘gamma’ also performed well, with a minimum error of 0.0169 and maximum error of 0.0216. These findings suggest that the pro-posed models have great potential for enhancing speech communication systems in various fields.

Demand Forecasting and Budget Planning for Automotive Supply Chain

Anand Limbare, Rashmi Agarwal — Thu, 30 Nov 2023 00:00:00 +0000

Over the past 20 years, there have been significant changes in the supply chain business. One of the most significant changes has been the development of supply chain management systems. It is now essential to use cutting-edge technologies to maintain competitiveness in a highly dynamic environment. Restocking inventories is one of a supplier’s main survival strategies and knowing what expenses to expect in the next month aids in better decision-making. This study aims to solve the three most common industry problems in Supply Chain – Inventory Management, Budget Fore-casting, and Cost vs Benefit of every supplier. The selection of the best forecasting model is still a major problem in much research in literature. In this context, this article aims to compare the performances of Auto-Regressive Integrated Moving Average (ARIMA), Holt-Winters (HW), and Long Short-Term Memory (LSTM) models for the prediction of a time series formed by the dataset of Supply Chain products. As performance measures, metric analysis of the Root Mean Square Error (RMSE) is used. The main concentration is on the Automotive Business Unit with the top 3 products under this segment and the country United States being in focus. All three models, ARIMA, HW, and LSTM obtained better results regarding the performance metrics.

Development and Simulation Two Wireless Hosts Communication Network Using Omnnet++

M. Derbali — Thu, 30 Nov 2023 00:00:00 +0000

A wireless network is a collection of computers and other electronic devices that exchange information by means of radio waves. Endpoint computing devices can all be connected without the need for hardwired data cabling thanks to the prevalence of wireless networks in today's businesses and networks. This paper's aim is to create and construct a wireless network model for connecting two hosts which will be implemented to simulate wireless communications. The sending of User Datagram Protocol (UPD) data by one of the hosts to the other one has been wirelessly specified by the simulator. Additionally, the protocol models were kept as simple as possible including both the physical layer and the lower layer. The architecture and functionality of a new simulator is showed its ability to solve the issues of making a host move, especially, when it gets out of the range the simulation ends.

Enhancing Real-time Object Detection with YOLO Algorithm

Gudala Lavanya, Sagar Dhanraj Pande — Tue, 05 Dec 2023 00:00:00 +0000

This paper introduces YOLO, the best approach to object detection. Real-time detection plays a significant role in various domains like video surveillance, computer vision, autonomous driving and the operation of robots. YOLO algorithm has emerged as a well-liked and structured solution for real-time object detection due to its ability to detect items in one operation through the neural network. This research article seeks to lay out an extensive understanding of the defined Yolo algorithm, its architecture, and its impact on real-time object detection. This detection will be identified as a regression problem by frame object detection to spatially separated bounding boxes. Tasks like recognition, detection, localization, or finding widespread applicability in the best real-world scenarios, make object detection a crucial subdivision of computer vision. This algorithm detects objects in real-time using convolutional neural networks (CNN). Overall this research paper serves as a comprehensive guide to understanding the detection of objects in real-time using the You Only Look Once (YOLO) algorithm. By examining architecture, variations, and implementation details the reader can gain an understanding of YOLO’s capability.

Comprehensive Analysis of Blockchain Algorithms

Prabhat Kumar Tiwari, Nidhi Agarwal, Shabaj Ansari, Mohammad Asif — Wed, 06 Dec 2023 00:00:00 +0000

INTRODUCTION: Blockchain technology has gained significant attention across various sectors as a distributed ledger solution. To comprehend its applicability and potential, a comprehensive understanding of blockchain's essential elements, functional traits, and architectural design is imperative. Consensus algorithms play a critical role in ensuring the proper operation and security of blockchain networks. Consensus algorithms play a vital role in maintaining the proper operation of a blockchain network, and their selection is crucial for optimal performance and security.

OBJECTIVES: The objective of this research is to analyse and compare various consensus algorithms based on their performance and efficiency in mining blocks.

METHODS: To achieve this, an experimental model was developed to measure the number of mined blocks over time for different consensus algorithms.

RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems.

CONCLUSION: The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. By enhancing our knowledge of consensus algorithms, this research aims to facilitate the development of more secure and efficient blockchain applications.

Speech Emotion Recognition using Extreme Machine Learning

Mon, 27 Nov 2023 00:00:00 +0000

Detecting Emotion from Spoken Words (SER) is the task of detecting the underlying emotion in spoken language. It is a challenging task, as emotions are subjective and highly contextual. Machine learning algorithms have been widely used for SER, and one such algorithm is the Gaussian Mixture Model (GMM) algorithm. The GMM algorithm is a statistical model that represents the probability distribution of a random variable as a sum of Gaussian distributions. It has been widely used for speech recognition and classification tasks. In this article, we offer a method for SER using Extreme Machine Learning (EML) with the GMM algorithm. EML is a type of machine learning that uses randomization to achieve high accuracy at a low computational cost. It has been effectively utilised in various classification tasks. For the planned approach includes two steps: feature extraction and emotion classification. Cepstral Coefficients of Melody Frequency (MFCCs) are used in order to extract features. MFCCs are commonly used for speech processing and represent the spectral envelope of the speech signal. The GMM algorithm is used for emotion classification. The input features are modelled as a mixture of Gaussians, and the emotion is classified based on the likelihood of the input features belonging to each Gaussian. Measurements were taken of the suggested method on the The Berlin Database of Emotional Speech (EMO-DB) and achieved an accuracy of 74.33%. In conclusion, the proposed approach to SER using EML and the GMM algorithm shows promising results. It is a computationally efficient and effective approach to SER and can be used in various applications, such as speech-based emotion detection for virtual assistants, call centre analytics, and emotional analysis in psychotherapy.

Milk Quality Prediction Using Machine Learning

Drashti Bhavsar, Yash Jobanputra, Nirmal Keshari Swain, Debabrata Swain — Wed, 29 Nov 2023 00:00:00 +0000

Milk is the main dietary supply for every individual. High-quality milk shouldn't contain any adulterants. Dairy products are sold everywhere in society. Yet, the local milk vendors use a wide range of adulterants in their products, permanently altering the evaporated. Using milk that has gone bad can have serious health consequences. On October 18 of this year, the Food Safety and Standards Authority of India (FSSAI), the nation's top food safety authority, released the final result of the National Milk Safety and Quality Survey (NMSQS) and declared the milk readily available in India to be "mostly safe." According to an FSSAI survey, 68.4% of the milk in India is tainted. The quality of milk cannot be checked by any equipment or special system. Milk that has not been pasteurized has not been treated to get rid of harmful bacteria. Infected raw milk may contain Salmonella, Campylobacter, Cryptosporidium, E. coli, Listeria, Brucella, and other dangerous pathogens. These microorganisms pose a major risk to your family's health. Manually analyzing the various milk constituents can be very challenging when determining the quality of the milk. Analyzing and discovering with the aid of machine learning can help with this endeavor. Here a machine learning-based milk quality prediction system is developed. The proposed technology has shown 99.99% classification accuracy.

Indian Budget 2022: A Make-or-Break Moment for Cryptocurrency

Tue, 05 Dec 2023 00:00:00 +0000

People are liable to the tax rate if they transfer digital assets during a specific fiscal year. There is no distinction between income from businesses and investments or between short-term and long-term gains because the 30% tax rate is applicable regardless of the sort of income. By clearly stating how it would be charged, the Indian budget 2022 has provided some direction. Losses were consequently experienced by both new and old cryptocurrency buyers. Under Section 115 BBH, it is illegal to offset cryptocurrency losses with cryptocurrency gains—or any other gains or revenue, for that matter. The implementation of the 30% tax rule on digital assets has caused the collapse of the cryptocurrency market, and there is a possibility that investors will continue to suffer losses in the future.

Security in Mobile Network: Issues, Challenges and Solutions

Ruby Dahiya, Anjali Kashyap, Bhupendra Sharma, Rahul Kumar Sharma, Nidhi Agarwal — Wed, 06 Dec 2023 00:00:00 +0000

INTRODUCTION: Mobile devices are integrated into daily activities of people's life. Compared to desktop computers the growth of mobile devices is tremendous in recent years. The growth of mobile devices opens vast scope for attackers on these devices.

OBJECTIVES: This paper presents a deep study of different types of security risks involved in mobile devices and mobile applications.

METHODS: In this paper we study various mechanisms of security risks for the mobile devices and their applications. We also study how to prevent these security risks in mobile devices.

RESULTS: Various solutions are provided in paper through which operators can protect the security and privacy of user data and keep their customers' trust by implementing these procedures.

CONCLUSION: This paper concludes with their solutions for providing a secure mobile network. This paper is structured as follows. Section 2 contains related work. Section 3 describes security problems. Section 4 discusses defensive methods and Section 5 gives the conclusion.

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Mallu Shiva Rama Krishna, Sudheer Mangalampalli — Wed, 06 Dec 2023 00:00:00 +0000

Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.